There is a desire to provide automated scoring systems for board games such as, GO, territory games and the like. The players may be all human players; all computer system players; or some human players and some computer system players.
Often the process of scoring a board configuration for this type of game is not straightforward and yet there is a desire to determine such scores quickly and accurately. For example, in the game of GO, determining the score at the end of the game involves assessing whether stones on the board are alive or dead. Accurately determining life or death is difficult; even very strong human players sometimes disagree over the life or death status of a group of stones.
In addition, scoring at the end of a game of GO is complicated by the fact that different scoring methods apply according to the rules being followed. The two main scoring methods are territory scoring and area scoring. Territory scoring, used by the Japanese rules, counts the surrounded territory plus the number of captured opponent stones. Area scoring, used by the Chinese rules, counts the surrounded territory plus the alive stones on the board. The result of the two methods is typically the same except in some particular circumstances involving so called seki positions or because one player placed more stones than the other. However, it is difficult to identify seki positions accurately using computer GO systems without great computational expense and/or complexity.
Previous attempts have been made to program computers to play Go. However, performance has not matched the level of chess programs even the weaker of which easily match the ability of an average club player. In contrast, the best Go programs play only at the level of weak amateur Go players and Go is therefore considered to be a serious AI (artificial intelligence) challenge not unlike Chess in the 1960s. There are two main reasons for this state of affairs: firstly, the high branching factor of Go (typically 200 to 300 potential moves per position) prevents the expansion of a game tree to any useful depth. Secondly, it is difficult to produce an evaluation function for Go positions. A Go stone has no intrinsic value; its value is determined by its relationships with other stones. Go players evaluate positions using visual pattern recognition and qualitative intuitions which are difficult to formalize.